Overview

Dataset statistics

Number of variables16
Number of observations1000000
Missing cells285329
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory122.1 MiB
Average record size in memory128.0 B

Variable types

Numeric9
Categorical7

Alerts

username has a high cardinality: 4325 distinct values High cardinality
birth_date has a high cardinality: 2440 distinct values High cardinality
title has a high cardinality: 12529 distinct values High cardinality
genre has a high cardinality: 4308 distinct values High cardinality
user_completed is highly correlated with user_days_spent_watchingHigh correlation
user_days_spent_watching is highly correlated with user_completedHigh correlation
score is highly correlated with scored_by and 2 other fieldsHigh correlation
scored_by is highly correlated with score and 2 other fieldsHigh correlation
rank is highly correlated with score and 2 other fieldsHigh correlation
popularity is highly correlated with score and 2 other fieldsHigh correlation
user_completed is highly correlated with user_days_spent_watchingHigh correlation
user_days_spent_watching is highly correlated with user_completedHigh correlation
score is highly correlated with rank and 1 other fieldsHigh correlation
rank is highly correlated with score and 1 other fieldsHigh correlation
popularity is highly correlated with score and 1 other fieldsHigh correlation
user_completed is highly correlated with user_days_spent_watchingHigh correlation
user_days_spent_watching is highly correlated with user_completedHigh correlation
score is highly correlated with rankHigh correlation
scored_by is highly correlated with popularityHigh correlation
rank is highly correlated with scoreHigh correlation
popularity is highly correlated with scored_byHigh correlation
my_score is highly correlated with scoreHigh correlation
user_completed is highly correlated with user_days_spent_watchingHigh correlation
user_days_spent_watching is highly correlated with user_completedHigh correlation
type is highly correlated with sourceHigh correlation
source is highly correlated with typeHigh correlation
score is highly correlated with my_score and 2 other fieldsHigh correlation
rank is highly correlated with score and 1 other fieldsHigh correlation
popularity is highly correlated with score and 1 other fieldsHigh correlation
birth_date has 262229 (26.2%) missing values Missing
rank has 23100 (2.3%) missing values Missing
df_index is uniformly distributed Uniform
df_index has unique values Unique
my_score has 155395 (15.5%) zeros Zeros

Reproduction

Analysis started2021-11-16 10:05:03.271582
Analysis finished2021-11-16 10:06:09.281509
Duration1 minute and 6.01 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500114.282
Minimum0
Maximum1000221
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:09.341523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50004.95
Q1250067.75
median500115.5
Q3750156.25
95-th percentile950218.05
Maximum1000221
Range1000221
Interquartile range (IQR)500088.5

Descriptive statistics

Standard deviation288731.5432
Coefficient of variation (CV)0.5773311293
Kurtosis-1.199960846
Mean500114.282
Median Absolute Deviation (MAD)250044.5
Skewness1.449976156 × 10-5
Sum5.00114282 × 1011
Variance8.336590402 × 1010
MonotonicityStrictly increasing
2021-11-16T11:06:09.458968image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
2399041
 
< 0.1%
1538861
 
< 0.1%
1477411
 
< 0.1%
1497881
 
< 0.1%
1600271
 
< 0.1%
1620741
 
< 0.1%
1559291
 
< 0.1%
1579761
 
< 0.1%
1354471
 
< 0.1%
Other values (999990)999990
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
10002211
< 0.1%
10002201
< 0.1%
10002191
< 0.1%
10002181
< 0.1%
10002171
< 0.1%
10002161
< 0.1%
10002151
< 0.1%
10002141
< 0.1%
10002131
< 0.1%
10002121
< 0.1%

username
Categorical

HIGH CARDINALITY

Distinct4325
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
MugenDreamu
 
5656
Pullman
 
4149
MistButterfly
 
3988
Jay-kun
 
3562
purplepinapples
 
3281
Other values (4320)
979364 

Length

Max length16
Median length9
Mean length8.94287
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)< 0.1%

Sample

1st rowkarthiga
2nd rowkarthiga
3rd rowkarthiga
4th rowkarthiga
5th rowkarthiga

Common Values

ValueCountFrequency (%)
MugenDreamu5656
 
0.6%
Pullman4149
 
0.4%
MistButterfly3988
 
0.4%
Jay-kun3562
 
0.4%
purplepinapples3281
 
0.3%
sprite19892682
 
0.3%
Myorave2356
 
0.2%
AnimeKami2310
 
0.2%
soccerscot152219
 
0.2%
-Ackerman2204
 
0.2%
Other values (4315)967593
96.8%

Length

2021-11-16T11:06:09.574994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mugendreamu5656
 
0.6%
pullman4149
 
0.4%
mistbutterfly3988
 
0.4%
jay-kun3562
 
0.4%
purplepinapples3281
 
0.3%
sprite19892682
 
0.3%
myorave2356
 
0.2%
animekami2310
 
0.2%
soccerscot152219
 
0.2%
ackerman2204
 
0.2%
Other values (4315)967593
96.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

anime_id
Real number (ℝ≥0)

Distinct12530
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10857.32272
Minimum1
Maximum37877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:09.676040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile145
Q11535
median6862
Q317729
95-th percentile32983
Maximum37877
Range37876
Interquartile range (IQR)16194

Descriptive statistics

Standard deviation10967.67552
Coefficient of variation (CV)1.010163905
Kurtosis-0.4652558961
Mean10857.32272
Median Absolute Deviation (MAD)6064
Skewness0.9087388412
Sum1.085732272 × 1010
Variance120289906.2
MonotonicityNot monotonic
2021-11-16T11:06:09.788078image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15352924
 
0.3%
15752362
 
0.2%
1992207
 
0.2%
2262159
 
0.2%
164982125
 
0.2%
29042087
 
0.2%
42242076
 
0.2%
117572056
 
0.2%
1212051
 
0.2%
65472051
 
0.2%
Other values (12520)977902
97.8%
ValueCountFrequency (%)
11492
0.1%
5741
0.1%
61082
0.1%
7326
 
< 0.1%
849
 
< 0.1%
15281
 
< 0.1%
16568
 
0.1%
1785
 
< 0.1%
18241
 
< 0.1%
19678
0.1%
ValueCountFrequency (%)
378771
< 0.1%
378751
< 0.1%
378671
< 0.1%
378541
< 0.1%
378531
< 0.1%
378451
< 0.1%
378251
< 0.1%
378011
< 0.1%
377921
< 0.1%
377901
< 0.1%

my_score
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.333114
Minimum0
Maximum10
Zeros155395
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:09.886093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median7
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.135274612
Coefficient of variation (CV)0.4950605045
Kurtosis-0.05730720258
Mean6.333114
Median Absolute Deviation (MAD)1
Skewness-1.060830093
Sum6333114
Variance9.829946893
MonotonicityNot monotonic
2021-11-16T11:06:09.966118image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
8208140
20.8%
7192612
19.3%
0155395
15.5%
9143384
14.3%
6105043
10.5%
1099906
10.0%
552298
 
5.2%
422587
 
2.3%
310602
 
1.1%
26259
 
0.6%
ValueCountFrequency (%)
0155395
15.5%
13774
 
0.4%
26259
 
0.6%
310602
 
1.1%
422587
 
2.3%
552298
 
5.2%
6105043
10.5%
7192612
19.3%
8208140
20.8%
9143384
14.3%
ValueCountFrequency (%)
1099906
10.0%
9143384
14.3%
8208140
20.8%
7192612
19.3%
6105043
10.5%
552298
 
5.2%
422587
 
2.3%
310602
 
1.1%
26259
 
0.6%
13774
 
0.4%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Male
712984 
Female
287016 

Length

Max length6
Median length4
Mean length4.574032
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowFemale

Common Values

ValueCountFrequency (%)
Male712984
71.3%
Female287016
28.7%

Length

2021-11-16T11:06:10.057760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-16T11:06:10.119791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
male712984
71.3%
female287016
28.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

user_completed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct798
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean579.644442
Minimum0
Maximum5674
Zeros1238
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:10.187805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q1194
median362
Q3696
95-th percentile1766
Maximum5674
Range5674
Interquartile range (IQR)502

Descriptive statistics

Standard deviation700.7791147
Coefficient of variation (CV)1.208980996
Kurtosis19.58249895
Mean579.644442
Median Absolute Deviation (MAD)211
Skewness3.765226088
Sum579644442
Variance491091.3677
MonotonicityNot monotonic
2021-11-16T11:06:10.290828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56745656
 
0.6%
1024955
 
0.5%
2174248
 
0.4%
41374149
 
0.4%
39233988
 
0.4%
32843562
 
0.4%
3493431
 
0.3%
28983281
 
0.3%
2653065
 
0.3%
5683005
 
0.3%
Other values (788)960660
96.1%
ValueCountFrequency (%)
01238
0.1%
1109
 
< 0.1%
2127
 
< 0.1%
3143
 
< 0.1%
4197
 
< 0.1%
5259
 
< 0.1%
6171
 
< 0.1%
7236
 
< 0.1%
8293
 
< 0.1%
9362
 
< 0.1%
ValueCountFrequency (%)
56745656
0.6%
41374149
0.4%
39233988
0.4%
32843562
0.4%
28983281
0.3%
23462356
0.2%
22982310
0.2%
21892219
 
0.2%
21762204
 
0.2%
20822082
 
0.2%

user_days_spent_watching
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3770
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean165.5766863
Minimum0
Maximum3185.71
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:10.403891image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.44
Q167.3
median116
Q3191.93
95-th percentile413.88
Maximum3185.71
Range3185.71
Interquartile range (IQR)124.63

Descriptive statistics

Standard deviation257.5896632
Coefficient of variation (CV)1.555712153
Kurtosis104.3334158
Mean165.5766863
Median Absolute Deviation (MAD)57.24
Skewness9.289707037
Sum165576686.3
Variance66352.43457
MonotonicityNot monotonic
2021-11-16T11:06:10.509902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3185.715656
 
0.6%
453.054149
 
0.4%
614.963988
 
0.4%
242.863985
 
0.4%
494.973562
 
0.4%
30.862682
 
0.3%
421.932356
 
0.2%
416.892310
 
0.2%
141.542309
 
0.2%
493.012219
 
0.2%
Other values (3760)966784
96.7%
ValueCountFrequency (%)
011
 
< 0.1%
0.031
 
< 0.1%
0.055
 
< 0.1%
0.069
 
< 0.1%
0.081131
0.1%
0.0947
 
< 0.1%
0.112
 
< 0.1%
0.141
 
< 0.1%
0.193
 
< 0.1%
0.22
 
< 0.1%
ValueCountFrequency (%)
3185.715656
0.6%
1332.14192
 
< 0.1%
919.011708
 
0.2%
813.61951
 
0.2%
720.36645
 
0.1%
698.5116
 
< 0.1%
636.09100
 
< 0.1%
614.963988
0.4%
590.1354
 
< 0.1%
587.091757
 
0.2%

birth_date
Categorical

HIGH CARDINALITY
MISSING

Distinct2440
Distinct (%)0.3%
Missing262229
Missing (%)26.2%
Memory size7.6 MiB
1991-01-01
 
5368
1992-01-16
 
4310
1989-10-22
 
4149
1989-07-29
 
3107
1994-01-01
 
3040
Other values (2435)
717797 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st row1990-04-29
2nd row1990-04-29
3rd row1990-04-29
4th row1990-04-29
5th row1990-04-29

Common Values

ValueCountFrequency (%)
1991-01-015368
 
0.5%
1992-01-164310
 
0.4%
1989-10-224149
 
0.4%
1989-07-293107
 
0.3%
1994-01-013040
 
0.3%
1989-01-012633
 
0.3%
1993-12-282550
 
0.3%
1995-01-012514
 
0.3%
1988-01-012452
 
0.2%
1992-05-142259
 
0.2%
Other values (2430)705389
70.5%
(Missing)262229
 
26.2%

Length

2021-11-16T11:06:10.614938image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1991-01-015368
 
0.7%
1992-01-164310
 
0.6%
1989-10-224149
 
0.6%
1989-07-293107
 
0.4%
1994-01-013040
 
0.4%
1989-01-012633
 
0.4%
1993-12-282550
 
0.3%
1995-01-012514
 
0.3%
1988-01-012452
 
0.3%
1992-05-142259
 
0.3%
Other values (2430)705389
95.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

title
Categorical

HIGH CARDINALITY

Distinct12529
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Death Note
 
2924
Code Geass: Hangyaku no Lelouch
 
2362
Sen to Chihiro no Kamikakushi
 
2207
Elfen Lied
 
2159
Shingeki no Kyojin
 
2125
Other values (12524)
988223 

Length

Max length100
Median length20
Mean length23.423018
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1893 ?
Unique (%)0.2%

Sample

1st rowOne Piece
2nd rowChobits
3rd rowGakuen Alice
4th rowFruits Basket
5th rowUltra Maniac

Common Values

ValueCountFrequency (%)
Death Note2924
 
0.3%
Code Geass: Hangyaku no Lelouch2362
 
0.2%
Sen to Chihiro no Kamikakushi2207
 
0.2%
Elfen Lied2159
 
0.2%
Shingeki no Kyojin2125
 
0.2%
Code Geass: Hangyaku no Lelouch R22087
 
0.2%
Toradora!2076
 
0.2%
Sword Art Online2056
 
0.2%
Fullmetal Alchemist2051
 
0.2%
Angel Beats!2051
 
0.2%
Other values (12519)977902
97.8%

Length

2021-11-16T11:06:10.722962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no280267
 
7.4%
the60685
 
1.6%
to49428
 
1.3%
movie42350
 
1.1%
ga35934
 
1.0%
wa33776
 
0.9%
32584
 
0.9%
ni27574
 
0.7%
of24807
 
0.7%
220916
 
0.6%
Other values (13134)3169876
83.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

type
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
TV
629300 
Movie
137701 
OVA
121403 
Special
83600 
ONA
 
19228

Length

Max length7
Median length2
Mean length2.998038
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV
2nd rowTV
3rd rowTV
4th rowTV
5th rowTV

Common Values

ValueCountFrequency (%)
TV629300
62.9%
Movie137701
 
13.8%
OVA121403
 
12.1%
Special83600
 
8.4%
ONA19228
 
1.9%
Music8768
 
0.9%

Length

2021-11-16T11:06:10.828010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-16T11:06:10.890023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
tv629300
62.9%
movie137701
 
13.8%
ova121403
 
12.1%
special83600
 
8.4%
ona19228
 
1.9%
music8768
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

source
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Manga
413257 
Original
182785 
Light novel
153030 
Visual novel
69260 
Unknown
48653 
Other values (11)
133015 

Length

Max length13
Median length5
Mean length7.281572
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManga
2nd rowManga
3rd rowManga
4th rowManga
5th rowManga

Common Values

ValueCountFrequency (%)
Manga413257
41.3%
Original182785
18.3%
Light novel153030
 
15.3%
Visual novel69260
 
6.9%
Unknown48653
 
4.9%
Novel35551
 
3.6%
Game35450
 
3.5%
4-koma manga28572
 
2.9%
Web manga13778
 
1.4%
Other11229
 
1.1%
Other values (6)8435
 
0.8%

Length

2021-11-16T11:06:10.966632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
manga456086
36.0%
novel257841
20.3%
original182785
14.4%
light153030
 
12.1%
visual69260
 
5.5%
unknown48653
 
3.8%
game37502
 
3.0%
4-koma28572
 
2.3%
web13778
 
1.1%
other11229
 
0.9%
Other values (6)8865
 
0.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

score
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct609
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.5121052
Minimum1.9
Maximum9.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:11.060674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile6.22
Q17.11
median7.56
Q38.04
95-th percentile8.59
Maximum9.25
Range7.35
Interquartile range (IQR)0.93

Descriptive statistics

Standard deviation0.7661365442
Coefficient of variation (CV)0.1019869296
Kurtosis2.942844054
Mean7.5121052
Median Absolute Deviation (MAD)0.47
Skewness-0.9603056362
Sum7512105.2
Variance0.5869652043
MonotonicityNot monotonic
2021-11-16T11:06:11.167691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.7512284
 
1.2%
8.219175
 
0.9%
7.419028
 
0.9%
7.888865
 
0.9%
7.48698
 
0.9%
7.898678
 
0.9%
7.428608
 
0.9%
7.458400
 
0.8%
7.838373
 
0.8%
8.297940
 
0.8%
Other values (599)909951
91.0%
ValueCountFrequency (%)
1.983
 
< 0.1%
2.0855
 
< 0.1%
2.33266
< 0.1%
2.3817
 
< 0.1%
2.441
 
< 0.1%
2.5236
 
< 0.1%
2.551
 
< 0.1%
2.571
 
< 0.1%
2.612
 
< 0.1%
2.6117
 
< 0.1%
ValueCountFrequency (%)
9.252012
0.2%
9.191120
0.1%
9.15572
 
0.1%
9.141744
0.2%
9.111646
0.2%
9.1174
 
< 0.1%
9.07323
 
< 0.1%
9.041056
0.1%
9.02350
 
< 0.1%
9.012363
0.2%

scored_by
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct5906
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112159.5922
Minimum1
Maximum1009477
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:11.281999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1716
Q117032
median53999
Q3142516
95-th percentile433097
Maximum1009477
Range1009476
Interquartile range (IQR)125484

Descriptive statistics

Standard deviation152686.8764
Coefficient of variation (CV)1.361335872
Kurtosis8.078199207
Mean112159.5922
Median Absolute Deviation (MAD)44896
Skewness2.550230367
Sum1.121595922 × 1011
Variance2.331328221 × 1010
MonotonicityNot monotonic
2021-11-16T11:06:11.395011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10094772924
 
0.3%
6277402362
 
0.2%
4986022207
 
0.2%
5146562159
 
0.2%
9402112125
 
0.2%
5439042087
 
0.2%
5578982076
 
0.2%
9159862056
 
0.2%
6418512051
 
0.2%
4914032051
 
0.2%
Other values (5896)977902
97.8%
ValueCountFrequency (%)
12
 
< 0.1%
24
 
< 0.1%
320
 
< 0.1%
417
 
< 0.1%
541
< 0.1%
655
< 0.1%
765
< 0.1%
887
< 0.1%
985
< 0.1%
1087
< 0.1%
ValueCountFrequency (%)
10094772924
0.3%
9402112125
0.2%
9159862056
0.2%
7335922012
0.2%
6918451526
0.2%
6593081467
0.1%
6486052042
0.2%
6418512051
0.2%
6277402362
0.2%
6232271404
0.1%

rank
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct9348
Distinct (%)1.0%
Missing23100
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean2091.111767
Minimum1
Maximum12857
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:11.503077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile68
Q1486
median1419
Q33059
95-th percentile6529
Maximum12857
Range12856
Interquartile range (IQR)2573

Descriptive statistics

Standard deviation2081.416661
Coefficient of variation (CV)0.9953636595
Kurtosis2.024101947
Mean2091.111767
Median Absolute Deviation (MAD)1094
Skewness1.459756785
Sum2042807085
Variance4332295.315
MonotonicityNot monotonic
2021-11-16T11:06:11.618997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2473726
 
0.4%
512924
 
0.3%
302362
 
0.2%
5602226
 
0.2%
192207
 
0.2%
12592175
 
0.2%
8232175
 
0.2%
5732167
 
0.2%
9822159
 
0.2%
1102125
 
0.2%
Other values (9338)952654
95.3%
(Missing)23100
 
2.3%
ValueCountFrequency (%)
12012
0.2%
21120
0.1%
3301
 
< 0.1%
4271
 
< 0.1%
51744
0.2%
6218
 
< 0.1%
7451
 
< 0.1%
8977
0.1%
9174
 
< 0.1%
10323
 
< 0.1%
ValueCountFrequency (%)
128574
< 0.1%
128562
< 0.1%
128552
< 0.1%
128541
 
< 0.1%
128523
< 0.1%
128511
 
< 0.1%
128491
 
< 0.1%
128481
 
< 0.1%
128471
 
< 0.1%
128461
 
< 0.1%

popularity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10381
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1441.811542
Minimum1
Maximum14484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-11-16T11:06:11.732025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q1250
median789
Q31893.25
95-th percentile5192
Maximum14484
Range14483
Interquartile range (IQR)1643.25

Descriptive statistics

Standard deviation1834.165217
Coefficient of variation (CV)1.272125492
Kurtosis8.438225745
Mean1441.811542
Median Absolute Deviation (MAD)650
Skewness2.542115478
Sum1441811542
Variance3364162.042
MonotonicityNot monotonic
2021-11-16T11:06:11.838063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12924
 
0.3%
942379
 
0.2%
92362
 
0.2%
392207
 
0.2%
162159
 
0.2%
2402141
 
0.2%
22125
 
0.2%
1292090
 
0.2%
222087
 
0.2%
132076
 
0.2%
Other values (10371)977450
97.7%
ValueCountFrequency (%)
12924
0.3%
22125
0.2%
32056
0.2%
42012
0.2%
51526
0.2%
61467
0.1%
72051
0.2%
81744
0.2%
92362
0.2%
102042
0.2%
ValueCountFrequency (%)
144841
< 0.1%
144771
< 0.1%
144751
< 0.1%
144661
< 0.1%
144651
< 0.1%
144641
< 0.1%
144631
< 0.1%
144601
< 0.1%
144581
< 0.1%
144531
< 0.1%

genre
Categorical

HIGH CARDINALITY

Distinct4308
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Hentai
 
12997
Comedy
 
8611
Slice of Life, Comedy
 
5894
Slice of Life, Comedy, School
 
5434
Music
 
5214
Other values (4303)
961850 

Length

Max length125
Median length37
Mean length37.582888
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230 ?
Unique (%)< 0.1%

Sample

1st rowAction, Adventure, Comedy, Super Power, Drama, Fantasy, Shounen
2nd rowSci-Fi, Comedy, Drama, Romance, Ecchi, Seinen
3rd rowComedy, School, Shoujo, Super Power
4th rowSlice of Life, Comedy, Drama, Romance, Fantasy, Shoujo
5th rowMagic, Comedy, Romance, School, Shoujo

Common Values

ValueCountFrequency (%)
Hentai12997
 
1.3%
Comedy8611
 
0.9%
Slice of Life, Comedy5894
 
0.6%
Slice of Life, Comedy, School5434
 
0.5%
Music5214
 
0.5%
Slice of Life, Comedy, Romance, School5005
 
0.5%
Action, Supernatural, Magic, Fantasy4847
 
0.5%
Comedy, Romance, School4225
 
0.4%
Comedy, Romance, School, Shounen3852
 
0.4%
Action, Sci-Fi, Adventure, Comedy, Fantasy, Shounen3807
 
0.4%
Other values (4298)940114
94.0%

Length

2021-11-16T11:06:11.962076image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy493421
 
10.3%
action390996
 
8.2%
romance299107
 
6.2%
drama281460
 
5.9%
fantasy246955
 
5.2%
shounen235599
 
4.9%
school232758
 
4.9%
supernatural231859
 
4.8%
adventure209332
 
4.4%
sci-fi197956
 
4.1%
Other values (36)1971975
41.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-11-16T11:06:01.404593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:30.972229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:35.061971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:38.797667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:42.465605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:46.156460image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:49.996327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:53.817407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:57.621103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:01.843676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:31.374355image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:35.465151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:39.198758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:42.880692image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:46.566546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:50.418421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:54.239502image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:58.051201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:02.273774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:31.786490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:35.889282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:39.597908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:43.295792image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:46.993821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:50.864074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:54.674655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:58.471872image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:02.695868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:32.188580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:36.295432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:40.007993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:43.688880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:47.400739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:51.284203image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:55.103025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:58.894995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:03.116962image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:32.594706image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:36.710526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:40.410090image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:44.096972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:47.801228image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:51.704847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:55.521119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:59.309095image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:03.541058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:33.021802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:37.130655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:40.828184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:44.508064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:48.210306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:52.121990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:55.942655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:59.731190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:03.969154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:33.437896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:37.551750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:41.239271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:44.923161image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:48.742484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:52.545087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:56.360785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:00.152312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:04.386248image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:33.861160image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:37.968395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:41.639418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:45.329275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:49.147575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:52.960180image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:56.776864image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:00.560405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:04.807342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:34.277254image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:38.387547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:42.061513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:45.745354image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:49.560381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:53.382309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:05:57.201973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T11:06:00.979500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-16T11:06:12.059112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-16T11:06:12.188126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-16T11:06:12.513199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-16T11:06:12.631225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-11-16T11:06:12.729046image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-16T11:06:05.340462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-16T11:06:06.228712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-16T11:06:07.770112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-16T11:06:08.168202image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexusernameanime_idmy_scoregenderuser_completeduser_days_spent_watchingbirth_datetitletypesourcescorescored_byrankpopularitygenre
00karthiga219Female4955.311990-04-29One PieceTVManga8.54423868.091.035.0Action, Adventure, Comedy, Super Power, Drama, Fantasy, Shounen
11karthiga597Female4955.311990-04-29ChobitsTVManga7.53175388.01546.0188.0Sci-Fi, Comedy, Drama, Romance, Ecchi, Seinen
22karthiga747Female4955.311990-04-29Gakuen AliceTVManga7.7733244.0941.01291.0Comedy, School, Shoujo, Super Power
33karthiga1207Female4955.311990-04-29Fruits BasketTVManga7.77167968.0939.0222.0Slice of Life, Comedy, Drama, Romance, Fantasy, Shoujo
44karthiga1787Female4955.311990-04-29Ultra ManiacTVManga7.269663.02594.02490.0Magic, Comedy, Romance, School, Shoujo
55karthiga2107Female4955.311990-04-29Ranma ½TVManga7.8559911.0802.0623.0Slice of Life, Comedy, Martial Arts, Fantasy
66karthiga2326Female4955.311990-04-29Cardcaptor SakuraTVManga8.21121898.0297.0292.0Adventure, Comedy, Drama, Magic, Romance, Fantasy, School, Shoujo
77karthiga2336Female4955.311990-04-29Daa! Daa! Daa!TVManga7.786598.0919.03045.0Comedy, Sci-Fi, Shoujo
88karthiga2498Female4955.311990-04-29InuYashaTVManga7.90181978.0697.0141.0Action, Adventure, Comedy, Historical, Demons, Supernatural, Magic, Romance, Fantasy, Shounen
99karthiga26910Female4955.311990-04-29BleachTVManga7.90433097.0693.018.0Action, Adventure, Comedy, Super Power, Supernatural, Shounen

Last rows

df_indexusernameanime_idmy_scoregenderuser_completeduser_days_spent_watchingbirth_datetitletypesourcescorescored_byrankpopularitygenre
9999901000212Sarovoc232817Male393130.751986-02-06Psycho-Pass 2TVOriginal7.54173042.01533.0180.0Action, Sci-Fi, Police, Psychological
9999911000213Sarovoc233276Male393130.751986-02-06Space☆Dandy 2nd SeasonTVOriginal8.2844045.0260.0992.0Space, Comedy, Sci-Fi
9999921000214Sarovoc237559Male393130.751986-02-06Nanatsu no TaizaiTVManga8.29385532.0251.052.0Action, Adventure, Supernatural, Magic, Ecchi, Fantasy, Shounen
9999931000215Sarovoc237757Male393130.751986-02-06Shingeki no Kyojin Movie 1: Guren no YumiyaMovieManga7.6918325.01139.01580.0Action, Drama, Fantasy, Shounen, Super Power
9999941000216Sarovoc237777Male393130.751986-02-06Shingeki no Kyojin Movie 2: Jiyuu no TsubasaMovieManga7.7613153.0974.01703.0Action, Super Power, Drama, Fantasy, Shounen
9999951000217Sarovoc240298Male393130.751986-02-06Shijou Saikyou no Deshi Kenichi SpecialsSpecialManga7.402500.02043.04420.0Action, Martial Arts, Comedy, School, Shounen
9999961000218Sarovoc244397Male393130.751986-02-06Kekkai SensenTVManga7.73166813.01041.0142.0Action, Comedy, Super Power, Supernatural, Vampire, Fantasy, Shounen
9999971000219Sarovoc246277Male393130.751986-02-06Yamada-kun to 7-nin no Majo (OVA)OVAManga7.5825763.01418.01545.0Comedy, Romance, School, Shounen
9999981000220Sarovoc247038Male393130.751986-02-06High School DxD BorNTVLight novel7.68191936.01148.0189.0Action, Comedy, Demons, Ecchi, Harem, Romance, School
9999991000221Sarovoc248338Male393130.751986-02-06Ansatsu KyoushitsuTVManga8.22313058.0289.059.0Action, Comedy, School, Shounen